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Background The majority of esophageal and gastric cancers are diagnosed at an advanced stage with poor overall survival (OS). Whether the pre‐diagnostic interval from symptom onset has any impact on OS is unclear. We investigated this question in the peri‐COVID19 pandemic era. Methods We retrospectively analyzed a cohort of 308 patients with esophageal, gastroesophageal junction, or gastric carcinoma treated with curative intent at the Princess Margaret Cancer Centre from January 2017 to December 2021. Clinical details pertaining to the initial presentation were determined through a retrospective chart review. Cox proportional hazards regression models were used to assess the association between pre‐diagnostic intervals and OS, adjusting for baseline patient characteristics. Results The median interval from symptom onset to diagnosis was 98 days (IQR 47–169 days). Using a cox proportional hazard model, prolonged pre‐diagnostic interval was not associated with worse OS (HR 1.00, p = 0.62). Comparing patients diagnosed before and during the COVID19 pandemic, there was a notable increase in diagnostic delay with median pre‐diagnostic interval increasing from 92 to 126 days (p = 0.007). Median age at time of diagnosis was 69.6 during the pandemic vs. 64.7 before the pandemic. Linear regression showed squamous cell histology was significantly associated with increasing time to initial diagnosis (p = 0.04), but this did not hold true in a multivariable model. Looking at other delay metrics, there were no changes in time interval from diagnosis to treatment during versus before the pandemic (median = 1.7 weeks for both), and there was no change in time from diagnosis to resection in those patients who underwent surgery. Conclusion The COVID19 pandemic caused significant diagnostic delay for patients presenting with curative gastroesophageal and gastric cancer. The lack of correlation of pre‐diagnostic interval with OS may reflect underlying tumor biology as the driving force that determines prognosis. The COVID19 pandemic caused significant diagnostic delay for patients presenting with curative gastroesophageal and gastric cancer. The lack of correlation of pre‐diagnostic interval with OS may reflect underlying tumor biology as the driving force that determines prognosis.

Background Patient-reported outcomes measures (PROM) are self-reflections of an individual’s physical functioning and emotional well-being. The Edmonton Symptom Assessment Scale (ESAS) is a simple and validated PRO tool of 10 common symptoms and a patient-reported functional status (PRFS) measure. The prognostic value of this tool is unknown in patients with gastroesophageal cancer (GEC). In this study, we examined the association between the ESAS score and overall survival (OS) in patients with GEC, the prognostication difference between ESAS and Eastern Cooperative Oncology Group (ECOG), and assessed the correlation between PRFS and the physician-reported ECOG performance status (PS). Methods The study was a retrospective cohort study of 211 patients with GEC with localized (stages I-III) and metastatic disease who completed at least one baseline ESAS prior to treatment. Patients were grouped into 3 cohorts based on ESAS score. OS was assessed using the Kaplan-Meier method, and the concordance index (c-index) was calculated for ESAS and physician-reported ECOG. The agreement between PRFS and physician-ECOG was also assessed. Results In total, 211 patients were included. The median age was 60.8 years; 90% of patients were ECOG PS 0-1; 38% of patients were stages I-III, while 62% were de novo metastatic patients. Median OS in low, moderate, high symptom burden (SB) patients’ cohorts was 19.17 m, 16.39 mm, and 12.68 m, respectively (P < .04). The ability to predict death was similar between physician-ECOG and ESAS (c-index 0.56 and 0.5753, respectively) and PRFS and physician-ECOG (c-index of 0.5615 and 0.5545, respectively). The PS agreement between patients and physicians was 50% with a weighted Kappa of 0.27 (95% CI: 0.17-0.38). Conclusion Patient’s SB seems to carry a prognostic significance. ESAS and physician-reported ECOG exhibit comparable prognostic values. Physicians and patients can frequently have divergent opinions on PS. ESAS takes a patient-centered approach and should be encouraged in practice among patients with GEC as an additional tool for prognostication. The prognostic value of the ESAS tool is unknown in gastroesophageal cancer. This study examined the association between the ESAS score and overall survival in patients with gastroesophageal cancer and assessed the correlation between patient-reported functional status and physician-reported ECOG performance status.

We investigated, whether 18[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29–91 years) undergoing 18[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients’ nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid 18[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer.

BackgroundTo investigate the relationship between attendance to a pre-treatment psychoeducational intervention (prehab) with treatment outcomes and toxicities in patients receiving radiotherapy for head and neck cancers (HNCs).MethodsPatients were included from prehab inception in 2013 to 2017, comparing overall survival (OS), locoregional recurrence-free survival (LRFS), and locoregional recurrence (LRR) between prehab attendees (PA) and non-attendees (PNA). Multivariable analysis was performed for OS and LRFS.ResultsAmong 864 PA and 1128 PNA, 2-year OS was 88% vs 80% (p < 0.001), and LRFS was 84% vs 75% (p < 0.001). On multivariable analysis (MVA), OS and LRFS were independently and unfavourably associated with PNA. The PA cohort had a lower frequency of a “rocky treatment course” compared with the PNA cohort (52/150, 35% vs 71/150, 47%; p = 0.034).ConclusionsPrehab at our institution is associated with improved long-term oncologic outcomes. Prospective data is needed to better understand this association.

We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline [sup.18] F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent [sup.18] F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, [sup.18] F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.

We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline 18F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent 18F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, 18F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.

We investigated, whether 18[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29−91 years) undergoing 18[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients’ nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid 18[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer.We investigated, whether 18[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29−91 years) undergoing 18[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients’ nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid 18[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer.

We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.

Our objective was to examine the impact of palliative care (PC) on aggressive care near death for patients with advanced pancreatic cancer. Measures of aggressive care included (i) chemotherapy within 14 days of death; (ii) more than one emergency department (ED) visit; (iii) more than one hospitalization; and (iv) at least one intensive care unit (ICU) admission, all within 30 days of death. A retrospective population-based cohort study was conducted with patients diagnosed with advanced pancreatic cancer in Ontario. Multivariable logistic analyses were performed. Our final cohort consisted of 5,381 patients (median survival of 75 days). 52% received a PC consultation. PC consultation was associated with decreased use of chemotherapy near death (OR=0.34); and lower risk of ICU admission (OR=0.12), multiple ED visits (OR=0.19), and multiple hospitalizations near death (OR=0.24). A per unit increase in the monthly rate of PC visits was associated with lower odds of aggressive care.